Triple

T15529028
Position Surface form Disambiguated ID Type / Status
Subject Strand E370161 entity
Predicate administrativeCentre P1474 FINISHED
Object Jørpeland
Jørpeland is a town in Rogaland county, Norway, known as a local industrial and service hub and a gateway to the nearby Lysefjord and Preikestolen (Pulpit Rock).
E1174702 NE FINISHED

How this triple was built (4 steps)

Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.

NER Named-entity recognition gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Jørpeland | Statement: [Strand, administrativeCentre, Jørpeland]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Jørpeland
Context triple: [Strand, administrativeCentre, Jørpeland]
  • A. Hjelmeland
    Hjelmeland is a rural municipality in southwestern Norway known for its fjord landscapes, agriculture, and traditional fruit farming.
  • B. Hadeland
    Hadeland is a traditional rural district in southeastern Norway known for its agricultural landscape, historic churches, and the Hadeland Glassverk glassworks.
  • C. Helgeland
    Helgeland is a coastal region in northern Norway known for its dramatic fjords, islands, and mountain landscapes.
  • D. Nordre Land
    Nordre Land is a rural municipality in Innlandet county, Norway, known for its forests, lakes, and agricultural landscape in the traditional district of Land.
  • E. Fjordane
    Fjordane is a traditional district in western Norway known for its dramatic fjord landscapes and coastal scenery.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Jørpeland
Triple: [Strand, administrativeCentre, Jørpeland]
Generated description
Jørpeland is a town in Rogaland county, Norway, known as a local industrial and service hub and a gateway to the nearby Lysefjord and Preikestolen (Pulpit Rock).
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Jørpeland
Target entity description: Jørpeland is a town in Rogaland county, Norway, known as a local industrial and service hub and a gateway to the nearby Lysefjord and Preikestolen (Pulpit Rock).
  • A. Hjelmeland
    Hjelmeland is a rural municipality in southwestern Norway known for its fjord landscapes, agriculture, and traditional fruit farming.
  • B. Hadeland
    Hadeland is a traditional rural district in southeastern Norway known for its agricultural landscape, historic churches, and the Hadeland Glassverk glassworks.
  • C. Helgeland
    Helgeland is a coastal region in northern Norway known for its dramatic fjords, islands, and mountain landscapes.
  • D. Nordre Land
    Nordre Land is a rural municipality in Innlandet county, Norway, known for its forests, lakes, and agricultural landscape in the traditional district of Land.
  • E. Fjordane
    Fjordane is a traditional district in western Norway known for its dramatic fjord landscapes and coastal scenery.
  • F. None of above. chosen

Provenance (5 batches)

The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.

Step Stage Batch ID Status When
creating Elicitation batch_69d85cc521a08190921fb50319dddc34 completed April 10, 2026, 2:13 a.m.
NER Named-entity recognition batch_69e0414620588190958ffde651ccab5f completed April 16, 2026, 1:54 a.m.
NED1 Entity disambiguation (via context triple) batch_69ff8759922c8190ae5a5700e3ecc87c completed May 9, 2026, 7:13 p.m.
NEDg Description generation batch_69ff87f0eed08190a0eaffb4a32f1eae completed May 9, 2026, 7:16 p.m.
NED2 Entity disambiguation (via description) batch_69ff88ba20788190aeadd14e56c510db completed May 9, 2026, 7:19 p.m.
Created at: April 10, 2026, 4:05 a.m.